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To overcome the problems of wind power forecast for the current deterministic wind power turbine output model cannot accurately describe the statistical characterization of wind turbine output, this article proposes a novel wind power stochastic cloud model. There are three steps to build this model. Firstly, it is necessary to analyze the power data and to filter out the disturbed data by utilizing the improved Bin method and data fitting. Secondly, it is available to obtain respectively two groups of the expectation, entropy and hyper entropy of the waist and upper data of stochastic cloud model by the known and unknown membership of backward cloud generators. Finally, the wind-speed data can be transformed into a stochastic cloud model, which is composed of X condition cloud generator and the positive cloud generator component. In short, the comparative data of the wind power cloud droplets and the measured data of wind power show that the line correlation coefficient of the wind power frequency of the proposed model reaches to 0.8868, which could simulate the statistical characterization of wind turbine output effectively.

This paper studies the reachable set estimation problem of a class of human-in-the-loop (HiTL) control systems described by a controlled hidden Markov jump model. Our aim is to determine an ellipsoid-like set which bounds all the machine state trajectories of the HiTL system under zero initial condition. By using a stochastic Lyapunov functional, a reachable set estimation condition is established, which is provided in terms of a set of matrix inequalities. Finally, an example is given to show the effectiveness of the proposed result.

Dynamic Movement Primitives (DMPs), which represent movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics, have been adapted for movement planning. In this paper, we present a trajectory generation method for mobile agent from multiple demonstration using DMPs. The original DMPs can be only used to learn from single demonstration. In order to adapt multiple demonstrations of different tasks, we use DMPs with Takagi-Sugeno fuzzy model to realize autonomous trajectory planning. The proposed scheme is capable to regenerate trajectory in the presence of obstacles even when the goal position is altered. A simulation example is given to confirm the effectiveness of the proposed scheme.

Staggered SAR is an advanced radar mode which can get the ability of high azimuth resolution and wide range swath simultaneously. Due to the serious spectrum-aliasing, the azimuth ambiguity occurs in the imaging result and brings the wrong amplitude and phase. To reconstruct the images without ambiguity, traditional model-driven methods and data-driven methods cannot have good performance on removing the artifacts and maintaining the real targets meanwhile. In this paper, we consider this task as a pixels process problem and then propose a pixel-wise deep reinforcement learning method to handle it. The proposed method obtains the best comprehensive performance comparing with other method. In addition, its validity is also verified in experiment.

The research is based on the characteristics of thematic learning website, from four dimensions of resource area, task area, interaction area and website running to explore on a feasible evaluation method for a thematic learning website and gives the evaluation standard for two grades of “excellent” and “pass”. Furthermore, further work is put forward. Firstly, evaluation on usability of thematic learning website should be designed. Secondly, students' interest in tasks exploratory and the acquisition of learning ability in a quantitative way are needed to be reflected in evaluation standard for thematic learning website.

Microgrid is a typical form of smart grid, which integrates distributed renewable energy sources (DRESs) to eliminate on-grid impact and achieve efficient applications. However, the real-time dispatch of microgrids is faced with challenges resulted by uncertainties of DRESs and nonlinear constraints. To deal with this problem, a real-time optimal dispatch strategy based on deep reinforcement learning (DRL) is proposed in this paper. Specifically, a detailed battery model is formulated, which considers the power loss during the discharge and charge process. Next, we formulate the real-time optimal dispatch problem as a Markov decision process, which consists of state, action, state transition, immediate reward and the action-value function. A Deep Deterministic Policy Gradient (DDPG) algorithm framework is designed to obtain the optimal dispatch strategy through off-line training and on-line decision making. Finally, the case study is performed based on real historical data of microgrid operation, the simulation results demonstrate the effectiveness and feasibility of the proposed method.

Stabilization is addressed for a class of stochastic Markovian reaction-diffusion systems (SMRDSs) via boundary-output-based asynchronous control strategy. Compared with the most existing results on the asynchronous control, a more general hidden Markov model is proposed to describe the asynchronous behavior. Both the transition rate matrix (TRM) and observation probability matrix (OPM) in the HMM are partly unknown and the incomplete TRM also contains uncertainty. Then an asynchronous boundary controller is designed only using boundary point information. Sufficient conditions are established for SMRDSs to ensure the mean square exponential stability with strictly (W, P, R)-$\alpha$ dissipative performance. A numerical example is presented to verify the effectiveness of the proposed boundary-output-based asynchronous control strategy.

In combination with optimization theory and swarm evolution mechanisms, this paper addresses a plume source localization issue based on multiple mobile robots equipped with sensors. First of all, we offer the plume modeling process and analyze some internal characteristics about its source. Secondly, an adaptive weight is introduced into quantum particle swarm optimization (QPSO) algorithm, which makes quantum-behaved optimization (QBO) algorithm be proposed as a swarm search scheme for robots. Subsequently, based on the attractive/repulsive field theory, a collision avoidance strategy with formation maintenance and obstacle elusion is provided for robots on the move. Finally, we offer the analysis for the developed policy, such as the global convergence and computational complexity.

During the operation of WPT (Wireless Power Transmission) system, load changes will cause the system's operating voltage and current to become unstable, and further cause circuit equipment to malfunction. A LCL-LCC type compensation network applied in wireless power transmission system was proposed in this paper. The circuit model was established to derive the working conditions of the primary side C-C (constant current) and secondary side C-V (constant voltage). And the current expression of the primary side and load voltage expression of the secondary side was obtained. The relationship between voltage gain of the system and capacitors of the secondary side was analyzed. In order to keep the current of the primary side and the load voltage of the secondary side stable when the circuit load changes, a circuit parameter design method with adaptive parameter configuration features was proposed which no control strategy is needed. Finally, the rationality of the proposed parameter design method was proved by the results of software simulation and experiment.

Branch prediction is an indispensable technology in modern processor design and a key method to improve processor performance. When the CPU is executing programs, branch instructions account for 20% to 30% of all instructions, which affect the processor performance. Although dynamic branch predictor based on global history has a simple structure and occupies less memory, the existence of index aliasing problem influences branch prediction accuracy. In this paper, the traditional Gselect branch predictor was improved by the XOR operation between the index generated by Gselect branch predictor and the last 8 bits of instruction’s PC address to ease the problem of index aliasing. Finally, the simulation was carried out by using SimpleScalar 3.0 simulator and benchmark programs. The experimental results show that when the length of GHR register is 12, the prediction rate of the newly improved branch predictor called XOR-Gselect is best and 1.93% higher than Gselect’s on average.

In the AES, it usually made up of four parts: AddRoundKey (ARK), SubBytes (SB), ShiftRows (SR), and MixColumns (MC). Among them, AddRoundKey, ShiftRows, and MixColumns are both linear operations, SubBytes is a nonlinear operation, which is the most complicated calculation. Moreover, S-box operations are also used in DES and SM4, so it has important research significance. Area optimization is particularly important in some chips with small area requirements. In order to reduce the hardware complexity, the finite field operation of the AES S-box is generally mapped to the composite field. At the same time, the design of the circuit using the dual basis will be smaller. In this paper, we propose an optimized design for the S-box in AES, which is mainly optimized for circuit area. The S-box is designed by using dual basis, and the circuit is simplified by AND-XOR array structure, so as to achieve the characteristics of small area. Compared with the previous works, the S-Box designed in this paper has a smaller area, which is 17.76% smaller than the previous S-Box. Compared to other basis, the S-Box are 29.87% and 25.34% smaller than that of the polynomial basis and normal basis respectively.